The standard asymptotic theory of likelihood ratio tests assumes that
you are testing a submodel, which is not the case here. Moreover, even
when testing submodels, there are other assumptions that often are not
met in the case of DLMs - the typical example being hypothesised
values on the
[mailto:gpet...@uark.edu]
Sent: Thu 10/8/2009 3:55 PM
To: Erb Philipp (erbp)
Cc: rhelp...@gmail.com; r-help@r-project.org
Subject: Re: [R] Evaluating/comparing dynamic linear model
The standard asymptotic theory of likelihood ratio tests assumes that
you are testing a submodel, which
Hi,
I have two DLM model specifications (x[t] and y[t] are univariate):
MODEL1:
y[t] = b[t]x[t]+e[t], e[t] ~ N(0,v1^2)
b[t] = b[t-1]+eta[t], eta[t] ~ N(0,w1^2)
MODEL2:
y[t] = a[t]+e[t], e[t] ~ N(0,v2^2)
a[t] = a[t-1]+eta[t], eta[t] ~ N(0,w2^2)
I run the filter through data recursively to
3 matches
Mail list logo
